Machine vision has been studied for about 40 years but the study in last 15 years has shown drastic progress due to the great advances in imaging and computing technologies. The main purpose of machine vision is to allow a computer to understand aspects of its environment using information provided by visual sensors. The subject of machine vision now embraces innumerable topics and applications: these range from automatic assembly and inspection to automatic vehicle guidance, from automatic documents interpretation to verification of signatures, and from analysis of remotely sensed images to checking of fingerprints and recognizing faces, to list just a few.
Automatic inspection and assembly is one of the areas where machine vision has been most successfully applied and it is still showing substantial growth. The necessity of improvements in quality, safety, and cost saving is the reason driving this growth. However, most successful techniques and their applications in this area have been confined to a specific type of environment where certain assumptions can be made about the scene. In typical manufacturing industries such as microelectronics fabrication for example, an image provides a scene of objects with pre-determined shape, structure, orientation, and so on, unless the position of a camera changes. In other words, images from such industries are deterministic. The primary goal of the inspection in such manufacturing industries is to check whether there are missing objects in pre-specified regions in the image or whether objects in the image are in desired orientations, and the necessary analysis is mainly done in image space.
An object of this invention is to provide a new field of application for machine vision to process industries. In this invention, machine vision will include new application areas and new tasks that have seldom been tried in contemporary machine vision research. New application areas include all process industries where the stochastic visual appearance of products or processes is a major concern. New tasks include estimation, modeling, control, and optimization of visual quality of the process or the product. Visual quality include textural appearance of processes and products. However, it can include spectral (i.e., color) and/or textural appearance of products and methodologies combining these two aspects have been proposed [MR-MIA].